The Frontiers of Peer-to-Peer Lending: Thinking About a New Regulatory Approach

The growth of online alternative lending presents several advantages for both those seeking credit and those with excess capital to lend. Over the past decade, several different models of peer-to-peer lending have emerged in the US and U.K. Each of these models has developed in response to the different regulatory system it faces, which has led to the models’ different risk and reward profiles. However, the current regulatory framework for regulating peer-to-peer lending, especially in the U.S., leaves much to be desired. The inadequate regulatory regime not only hampers the potential for growth and further innovation in the industry, but also creates risks for consumers, lenders, and, as the sector grows, entire markets. There is no clear or easy answer as to the optimal regulatory regime, but regulators should at least consider the basic functions of peer-to-peer lending and how to address risks with a more comprehensive and sensible model for regulation

Peer-to-peer lending and community development finance

Peer-to-peer (P2P) networks directly connect computer users online. Popular P2P platforms include eBay and Craigslist, for example, which have transformed the market for used consumer goods in recent years. Increasingly popular, however, are P2P lending sites that facilitate debt transactions by directly connecting borrowers and lenders on the Internet. In the summer of 2008, the Center for Community Development Investments assembled a working group of community development leaders, investors, and Prosper Marketplace, the largest P2P lending platform in the world, to discuss the potential community development implications of the innovation. This working paper documents this discussion and explores P2P lending in greater detail. Part I offers background on P2P and the state of the P2P lending industry; Part II outlines the potential community development finance implications of P2P; and Part III discusses the working group and next steps necessary to successfully marry P2P technology and community development finance.Loans ; Internet

Success Factors in Peer-to-Business (P2B) Crowdlending: A Predictive Approach

Peer-to-Business (P2B) crowdlending is gaining importance among companies seeking funding. However, not all projects get the same take-up by the crowd. Thus, this study aims to determine the key factors that drive non-professional investors to choose a given loan in an online environment. To this purpose, we have analyzed 243 crowdlending campaigns on October.eu platform. We have obtained a series of variables from the analyzed loans using logistic regression. Results indicate that loan amount, loan term and overall credit rating are the key predictors of non-professional lender P2B crowdlending success. These findings may be useful for predicting whether the crowd will subscribe to a loan request or not. This information would help businesses to modify specific loan characteristics (if possible) to make their loans more attractive or could even lead companies to consider a different financial option. It could also help platforms select and adapt project parameters to secure their success

Does Gender Affect Investors' Appetite for Risk?: Evidence from Peer-to-Peer Lending

This study investigates the role of gender in financial risk-taking. Specifically, I ask whether female investors tend to fund less risky investment projects than males. To answer this question, I use real-life investment data collected at the largest German market for peer-to-peer lending. Investors' utility is assumed to be a function of the projects expected return and its standard deviation, whereas standard deviation serves as a measure of risk. Gender differences regarding the responses to projects' risk are tested by estimating a random parameter regression model that allows for variation of risk preferences across investors. Estimation results provide no evidence of gender differences in investors' risk propensity: On average, male and female investors respond similarly to the changes in the standard deviation of expected return. Moreover, no differences between male and female investors are found with respect to other characteristics of projects that may serve as a proxy for projects' risk. Significant gender differences in investors' tastes are found only with respect to preferred investment duration, purpose of investment project and borrowers' age.gender, investment choice, risk preferences

Marketplace lending and its chances and risks for key stakeholders

The P2P lending market has significantly increased during the last few years. In Switzerland, the credit volume has more than doubled in just one year. However, in an international comparison China and the United Kingdom take on the leading position in the booming market. Analysts predict that the P2P loan market reaches one trillion U.S dollars by the year 2050. Apart from that, the introduction of alternative credit provision challenges the traditional financing models. Hence, there must be several benefits for preferring a P2P loan instead of applying and investing via the traditional way. Despite the benefits, the recent developments have revealed risks which potentially have farreaching consequences. The global credit market cannot afford to ignore the influence of the P2P lending and therefore a deep understanding of the underlying processes and potentials are required. Therefore, this thesis investigates the attributes of marketplace lending and whether the risks outweigh the chances for key stakeholders. In order to answer the research question, the thesis follows a qualitative approach. Existing literature is reviewed, compared and extended through a back and forth theory search. Furthermore, an interview with the Swiss platform Cashare was conducted. Due to the increased popularity and pressure of crowdlending platforms, it was not possible to conduct further interviews. Nevertheless, based on the literature review scenarios and assumptions are elaborated in order to carefully evaluate opportunities and risks for the three key stakeholders. The findings demonstrate high profitable returns on a low-cost basis as main benefits for investors. However, high returns simultaneously imply higher risks such as credit and platform risks, which are valued to be the most significant. Borrowers, which represents the counterpart, are profiting from an inexpensive and convenient financing alternative. Debtors are not directly involved in risks but rather affected by uncertainties occurring from the market or the platform. The decentralization of credit risks, low-cost structure and automated processes are valued to be competitive advantages for a P2P platform. However, the big data approach used for the credit assessment as well as the cost structure first need to prove their potential during a full economic cycle. Besides that, a marketplace lender primarily has to deal with reputational and operational risks

Loan Default Prediction: A Complete Revision of LendingClub

Predicción del default: Una revisión completa de LendingClub El objetivo del estudio es determinar un modelo de predicción de default crediticio usando la base de datos de LendingClub. La metodología consiste en estimar las variables que influyen en el proceso de predicción de préstamos pagados y no pagados utilizando el algoritmo Random Forest. El algoritmo define los factores con mayor influencia sobre el pago o el impago, generando un modelo reducido a nueve predictores relacionados con el historial crediticio del prestatario y el historial de pagos dentro de la plataforma. La medición del desempeño del modelo genera un resultado F1 Macro Score con una precisión mayor al 90% de la muestra de evaluación. Las contribuciones de este estudio incluyen, el haber utilizado la base de datos completa de toda la operación de LendingClub disponible, para obtener variables trascendentales para la tarea de clasificación y predicción, que pueden ser útiles para estimar la morosidad en el mercado de préstamos de persona a persona. Podemos sacar dos conclusiones importantes, primero confirmamos la capacidad del algoritmo Random Forest para predecir problemas de clasificación binaria en base a métricas de rendimiento obtenidas y segundo, denotamos la influencia de las variables tradicionales de puntuación de crédito en los problemas de predicción por defecto.The study aims to determine a credit default prediction model using data from LendingClub. The model estimates the effect of the influential variables on the prediction process of paid and unpaid loans. We implemented the random forest algorithm to identify the variables with the most significant influence on payment or default, addressing nine predictors related to the borrower's credit and payment background. Results confirm that the model’s performance generates a F1 Macro Score that accomplishes 90% in accuracy for the evaluation sample. Contributions of this study include using the complete dataset of the entire operation of LendingClub available, to obtain transcendental variables for the classification and prediction task, which can be helpful to estimate the default in the person-to-person loan market. We can draw two important conclusions, first we confirm the Random Forest algorithm's capacity to predict binary classification problems based on performance metrics obtained and second, we denote the influence of traditional credit scoring variables on default prediction problems

Determinants of default in p2p lending: the Mexican case

P2P lending is a new method of informal finance that uses the internet to directly connect borrowers with on-line communities. With a unique dataset provided by Prestadero, the largest on-line lending platform with national presence in Mexico, this research explores the effect of credit scores and other variables related to loan and borrower´s traits, in determining default behavior in P2P lending. Moreover, using a logistic regression model, it tested whether investors might benefit from screening loan applicants by gender after controlling for loan quality. The results showed that information provided by the platform is relevant for analyzing credit risk, yet not conclusive. In congruence with the literature, on a scale going from the safest to the riskiest, loan quality is positively associated with default behavior. Other determinants for increasing the odds of default are the payment-to-income ratio and refinancing on the same platform. On the contrary loan purpose and being a female applicant reduce such odds. No categorical evidence for differential default behavior was found for gender´s case-discrimination, under equal credit conditions. However it was found that controlling for loan quality, women have longer loan survival times than men. This is one of the first studies about debt crowdfunding in Latin America and Mexico. Implications for lenders, researchers and policy-makers are also discussed

Crowdlending: mapping the core literature and research frontiers

[EN] Peer-to-peer (P2P) lending uses two-sided platforms to link borrowers with a crowd of lenders. Despite considerable diversity in crowdlending research, studies in this area typically focus on several common research topics, including information asymmetries, social capital, communication channels, and rating-based models. This young research field is still expanding. However, its importance has increased considerably since 2018. This rise in importance suggests that P2P lending may offer a promising new scientific research field. This paper presents a bibliometric study based on keyword co-occurrence, author and reference co-citations, and bibliographic coupling. The paper thus maps the key features of P2P lending research. Although many of the most cited papers are purely financial, some focus on behavioral finance. The trend in this field is toward innovative finance based on new technologies. The conclusions of this study provide valuable insight for researchers, managers, and policymakers to understand the current and future status of this field. The variables that affect new financial contexts and the strategies that promote technology-based financial environments must be investigated in the future.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.Ribeiro-Navarrete, S.; Piñeiro-Chousa, J.; López-Cabarcos, MÁ.; Palacios Marqués, D. (2022). Crowdlending: mapping the core literature and research frontiers. Review of Managerial Science. 16(8):2381-2411. https://doi.org/10.1007/s11846-021-00491-82381241116

Crowdlending: mapping the core literature and research frontiers

Peer-to-peer (P2P) lending uses two-sided platforms to link borrowers with a crowd of lenders. Despite considerable diversity in crowdlending research, studies in this area typically focus on several common research topics, including information asymmetries, social capital, communication channels, and rating-based models. This young research field is still expanding. However, its importance has increased considerably since 2018. This rise in importance suggests that P2P lending may offer a promising new scientific research field. This paper presents a bibliometric study based on keyword co-occurrence, author and reference co-citations, and bibliographic coupling. The paper thus maps the key features of P2P lending research. Although many of the most cited papers are purely financial, some focus on behavioral finance. The trend in this field is toward innovative finance based on new technologies. The conclusions of this study provide valuable insight for researchers, managers, and policymakers to understand the current and future status of this field. The variables that affect new financial contexts and the strategies that promote technology-based financial environments must be investigated in the futureOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer NatureS